This paper presents an Artificial Intelligence (AI) integrated approach to Brain-Computer Interface (BCI)-based wheelchair development, utilizing a motor imagery right-left-hand movement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left-hand movements using electroencephalogram (EEG) data. A pre-filtered dataset, obtained from an open-source EEG repository, was segmented into arrays of 19x200 to capture the onset of hand movements. The data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter-based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose a framework that uses Convolutional Neural Network-Transformer Hybrid Model, named CTHM, for motor imagery EEG classification. The model achieves a test accuracy of 91.73% compared with various machine learning baseline models, including XGBoost, EEGNet, and a transformer-based model. The CTHM achieved a mean accuracy of 90% through stratified cross-validation, showcasing the effectiveness of the CNN-Transformer hybrid architecture in BCI applications.

翻译：本文提出了一种人工智能（AI）与脑机接口（BCI）轮椅开发相融合的方法，利用左右手运动想象机制进行控制。该系统旨在基于左右手运动想象的脑电图（EEG）数据，模拟轮椅的导航过程。从开源脑电数据集中获取经过预滤波的数据集，并将其分割为19×200的数组，以捕捉手部运动起始时刻。数据采样频率为200Hz。系统集成了基于Tkinter的界面用于模拟轮椅运动，为用户提供功能完备且直观的控制系统。我们提出了一种基于卷积神经网络-变压器混合模型（命名为CTHM）的运动想象脑电分类框架。该模型的测试准确率达到91.73%，优于包括XGBoost、EEGNet和基于Transformer的模型在内的多种机器学习基线模型。通过分层交叉验证，CTHM的平均准确率达到90%，验证了CNN-Transformer混合架构在脑机接口应用中的有效性。